Abstract
Exascale systems will contain multicore/manycore processors with high core count in each node. Therefore, using a model that relaxes the synchronization, such as data-flow, is crucial to adequately exploit the potential of the hardware. The flexibility of the data-flow execution model relies on the dynamic management of data-dependences among tasks.
The OpenMP standard already provides a construct, known as taskloop, that distributes the loop iteration space into several tasks, but this construct does not support the use of the depend clause yet. In this paper we propose the use of the induction variable to define data dependences in tasks created by the taskloop construct. By using the induction variable, each task will contain its own dependences based on the partition of work they received.
We also aim to demonstrate that using taskloop with dependences provides an enhancement in terms of programmability with respect to using stand-alone tasks to parallelize a loop. Our implementation does not introduce any significant overhead on the taskloop implementation and, in certain cases, it outperforms the stand-alone task version.
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References
Augonnet, C., Thibault, S., Namyst, R., Wacrenier, P.A.: StarPU: a unified platform for task scheduling on heterogeneous multicore architectures. Concur. Comput. Pract. Exp. 23(2), 187–198 (2011)
Ayguade, E., et al.: A proposal to extend the OpenMP tasking model for heterogeneous architectures. In: Müller, M.S., de Supinski, B.R., Chapman, B.M. (eds.) IWOMP 2009. LNCS, vol. 5568, pp. 154–167. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02303-3_13
Barcelona Supercomputing Center: Mercurium Compiler. https://github.com/bsc-pm/mcxx. Accessed 24 March 2019
Barcelona Supercomputing Center: Nanos6 Runtime. https://github.com/bsc-pm/nanos6. Accessed 24 March 2019
Wheeler, D.A.: SLOCCount: More about COCOMO. https://dwheeler.com/sloccount/sloccount.html#cocomo. Accessed 05 July 2021
Duran, A., Duran, A., et al.: OmpSs: a proposal for programming heterogeneous multi-core architectures. Parallel Process. Lett. 21(2), 173–193 (2011)
Intel: Intel C++ Compiler 19.0 Developer Guide and Reference. https://software.intel.com/en-us/cpp-compiler-developer-guide-and-reference-cilk-for. Accessed 24 March 2019
Luebke, D.: Cuda: scalable parallel programming for high-performance scientific computing. In: 2008 5th IEEE international symposium on biomedical imaging: from nano to macro, pp. 836–838. IEEE (2008)
Nguyen, V., Deeds-rubin, S., Tan, T., Boehm, B.: A SLOC counting standard. In: COCOMO II Forum 2007 (2007)
Nvidia: CUDA C++ Programming Guide 11.4 (June 2021). https://docs.nvidia.com/cuda/archive. Accessed 05 July 2021
OpenMP Architecture Review Board: OpenMP Application Programming Interface 4.5 (Nov 2015). Accessed 18 Feb 2021
OpenMP Architecture Review Board: OpenMP Application Programming Interface (Nov 2018). https://www.openmp.org/wp-content/uploads/OpenMP-API-Specification-5.0.pdf. Accessed 24 Mar 2019
Perez, J.M., Beltran, V., Labarta, J., Ayguadé, E.: Improving the integration of task nesting and dependencies in OpenMP. In: 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 809–818. IEEE (2017)
Sala, K., et al.: Improving the interoperability between MPI and task-based programming models. In: Proceedings of the 25th European MPI Users’ Group Meeting, pp. 1–11 (2018)
Sala, K., Teruel, X., Perez, J.M., Peña, A.J., Beltran, V., Labarta, J.: Integrating blocking and non-blocking MPI primitives with task-based programming models. Parallel Comput. 85, 153–166 (2019)
Shirako, J., Unnikrishnan, P., Chatterjee, S., Li, K., Sarkar, V.: Expressing doacross loop dependences in OpenMp. In: 9th International Workshop on OpenMP, IWOMP 2013, vol. 8122, pp. 30–44 (Sep 2013). https://doi.org/10.1007/978-3-642-40698-0
Watson, A.H., McCabe, T.J.: Structured testing: A testing methodology using the cyclomatic complexity metric, vol. 500, no. 235. Technical report., NIST Special Publication (1996)
Wheeler, David A.: SLOCCount. https://dwheeler.com/sloccount. Accessed 24 March 2019
Yin, T.: Lizard. https://github.com/terryyin/lizard. Accessed 24 March 2019
Acknowledgements
This research has received funding from the European Union’s Horizon 2020/EuroHPC research and innovation programme under grant agreement No 955606 (DEEP-SEA); and the support of the Spanish Ministry of Science and Innovation (Computacion de Altas Prestaciones VIII: PID2019-107255GB).
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Maroñas, M., Teruel, X., Beltran, V. (2021). OpenMP Taskloop Dependences. In: McIntosh-Smith, S., de Supinski, B.R., Klinkenberg, J. (eds) OpenMP: Enabling Massive Node-Level Parallelism. IWOMP 2021. Lecture Notes in Computer Science(), vol 12870. Springer, Cham. https://doi.org/10.1007/978-3-030-85262-7_4
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